City Statistics - OECD FUA Analysis

City Statistics - OECD FUA Analysis is a data analysis project focused on the economic, demographic, and environmental characteristics of Functional Urban Areas (FUA) across OECD countries.

The project uses advanced R scripting to clean, analyze, and visualize real-world datasets, addressing important research questions about GDP, labor markets, pollution, and urban development.

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🌐 Project Overview

• Preprocessing, cleaning, and integration of OECD's City Statistics datasets.
• Exploratory Data Analysis (EDA) with statistical graphics and heatmaps.
• In-depth analysis of:
  • Correlation between GDP per capita and PM2.5 pollution.
  • Impact of labor force, unemployment, and population on GDP per capita.
  • Temporal evolution of GDP.
  • Clustering of countries based on socio-economic and environmental indicators.
  • Trends in air quality (PM2.5 exposure) from 2009 to 2019.
• Fully reproducible R environment using renv.

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🛠️ Technologies & Tools

• Programming Language: R
• Environment Management: renv
• Data Analysis & Visualization: tidyverse, ggplot2, dplyr, tidyr, cluster, factoextra
• Document Preparation: RMarkdown, LaTeX (for PDF report generation)

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📁 Repository Structure

City-Statistics-Analysis/
├── docs/                    → Project documentation and presentations
├── dataset/                 → Raw, cleaned, and temporary datasets
├── misurazioni/             → Measurement results (e.g., distance matrices)
├── script/                  → R scripts organized by task
├── renv/                    → Environment management folder
├── .Rprofile                → Project-specific R profile settings
├── CityStatistics.Rproj     → RStudio project file
├── renv.lock                → Locked package versions for reproducibility
├── percentuale_media_per_citta_variabili_con_NA.csv  → Imputation statistics
├── README.md                → This file

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🚀 How to Use

1. Open the project with CityStatistics.Rproj in RStudio.
2. Restore the project environment by running renv::restore().
3. Explore the scripts inside script/:
   • EDA/ for exploratory data analysis.
   • Research_Questions/ for scripts answering each research question.
   • Clustering/ and PM25_Analysis/ for advanced studies.
4. Review the documentation inside docs/ for detailed methodology and results.

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📅 Research Questions Addressed

1. Is there a correlation between GDP per capita and mean PM2.5 exposure?
2. How do labor force, unemployment, and population jointly impact GDP per capita?
3. How has GDP evolved over time across cities?
4. Can countries be grouped into clusters based on socio-economic and environmental indicators?
5. How has PM2.5 exposure changed between 2009 and 2019?

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📄 Documentation

• City Statistics Final Report (PDF)
• Project Presentation (PPTX)

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👥 Contributors

• Di Maio Marco
• Somma Pasquale

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📄 License

This project is licensed under the CC BY-NC-SA 4.0 License

You may share and adapt this work for non-commercial purposes only, as long as you give appropriate credit and distribute your contributions under the same license.
For commercial use, explicit permission from the authors is required.